There are very few phrases in all of history that have done more damage to the project of software development than:
"Premature optimization is the root of all evil."
First, let's not besmirch the good name of Tony Hoare. The quote is from Donald Knuth, and the missing context is essential.
From his 1974 paper, "Structured Programming with go to Statements":
"Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%."
He was talking about using GOTO statements in C. He was talking about making software much harder to reason about in the name of micro-optimizations. He assumed (incorrectly) that we would respect the machines our software runs on.
Multiple generations of programmers have now been raised to believe that brutally inefficient, bloated, and slow software is just fine. There is no limit to the amount of boilerplate and indirection a computer can be forced to execute. There is no ceiling to the crystalline abstractions emerging from these geniuses. There is no amount of time too long for a JVM to spend starting.
I worked at Google many years ago. I have lived the absolute nightmares that evolve from the willful misunderstanding of this quote.
No thank you. Never again.
I have committed these sins more than any other, and I'm mad as hell about it.
Another one from my personal experience: apply DRY principles (don't repeat yourself) the third time you need something. Or in other words: you're allowed to copy-and-paste the same piece of code in two different places.
Far too often we generalise a piece of logic that we need in one or two places, making things more complicated for ourselves whenever they inevitably start to differ. And chances are very slim we will actually need it more than twice.
Premature generalisation is the most common mistake that separates a junior developer from an experienced one.
I think we should not even generalize it down to a rule of three, because then you're outsourcing your critical thinking to a rule rather than doing the thinking yourself.
Instead, I tend to ask: if I change this code here, will I always also need to change it over there?
Copy-paste is good as long as I'm just repeating patterns. A for loop is a pattern. I use for loops in many places. That doesn't mean I need to somehow abstract out for loops because I'm repeating myself.
But if I have logic that says that button_b.x = button_a.x + button_a.w + padding, then I should make sure that I only write that information down once, so that it stays consistent throughout the program.
At the risk of being "kicked out of engineering" I say "fuck all that bullshit, copy code all over the place, make it obvious what is happening, so obvious the self taught tutorial idiots that the boss insists we hire can figure it out." The industry is now filled with people that should be be writing code, and they vouch or one another in a cluster fuck of incompency. No wonder vide coding it taking over, nobody can think shit though anymore.
Ehh, people who are really excited about DRY write unreadable convoluted code, where the bulk of the code is abstractions invented to avoid rewriting a small amount of code and unless you're very familiar with the codebase reasoning about what it actually does is a mystery because related pieces of functionality are very far away from each other.
Oh yes, I'd recommend everyone who uses the phrase reads the rest of the paper to see the kinds of optimisations that Knuth considers justified. For example, optimising memory accesses in quicksort.
This shows how hard it is to create a generalized and simple rule regarding programming. Context is everything and a lot is relative and subjective.
Tips like "don't try to write smart code" are often repeated but useless (not to mention that "smart" here means over-engineered or overly complex, not smart).
Totally agree. I’ve see that quote used to justify wilfully ignoring basic performance techniques. Then people are surprised when the app is creaking exactly due to the lack of care taken earlier. I would tend to argue the other way most of the time: a little performance consideration goes a long way!
Maybe I’ve had an unrepresentative career, but I’ve never worked anywhere where there’s much time to fiddle with performance optimisations, let alone those that make the code/system significantly harder to understand. I expect that’s true of most people working in mainstream tech companies of the last twenty years or so. And so that quote is basically never applicable.
Slow code is more of a project management problem. Features are important and visible on the roadmap. Performance usually isn't until it hits "unacceptable", which may take a while to feed back. That's all it is.
(AI will probably make this worse as well, having a bloat tendency all of its own)
> Multiple generations of programmers have now been raised to believe that brutally inefficient, bloated, and slow software is just fine. There is no limit to the amount of boilerplate and indirection a computer can be forced to execute. There is no ceiling to the crystalline abstractions emerging from these geniuses. There is no amount of time too long for a JVM to spend starting.
I think that's due to people doing premature optimization! If people took the quote to heart, they would be less inclined to increasing the amount of boilerplate and indirection.
While you were seeing those problems with Java at Google, I saw seeing it with Python.
So many levels of indirection. Holy cow! So many unneeded superclasses and mixins! You can’t reason about code if the indirection is deeper than the human mind can grasp.
There was also a belief that list comprehensions were magically better somehow and would expand to 10-line monstrosities of unreadable code when a nested for loop would have been more readable and just as fast but because list comprehensions were fetishized nobody would stop at their natural readability limits. The result was like reading the run-on sentence you just suffered through.
In all honesty, this is one of the less abused quotes, and I have seen more benefit from it than harm.
Like you, I've seen people produce a lot of slow code, but it's mostly been from people who would have a really hard time writing faster code that's less wrong.
I hate slow software, but I'd pick it anytime over bogus software. Also, generally, it's easier to fix performance problems than incorrect behavior, especially so when the error has created data that's stored somewhere we might not have access to. But even more so, when the harm has reached the real world.
User-facing, sure, nothing stopping us from doing "simple and fast" software. But when it comes to the code, design and architecture, "simple" is often at odds with "fast", and also "secure". Once you need something to be fast and secure, it often leads to a less simple design, because now you care about more things, it's kind of hard to avoid.
Same. I, too, am sick of bloated code. But I use the quote as a reminder to myself: "look, the fact that you could spend the rest of the workday making this function run in linear instead of quadratic time doesn't mean you should – you have so many other tasks to tackle that it's better that you leave the suboptimal-but-obviously-correct implementation of this one little piece as-is for now, and return to it later if you need to".
I think it's fine and generous that he credited these rules to the better-known aphorisms that inspired them, but I think his versions are better, they deserve to be presented by themselves, instead of alongside the mental clickbait of the classic aphorisms. They preserve important context that was lost when the better-known versions were ripped out of their original texts.
For example, I've often heard "premature optimization is the root of all evil" invoked to support opposite sides of the same argument. Pike's rules are much clearer and harder to interpret creatively.
Also, it's amusing that you don't hear this anymore:
> Rule 5 is often shortened to "write stupid code that uses smart objects".
In context, this clearly means that if you invest enough mental work in designing your data structures, it's easy to write simple code to solve your problem. But interpreted through an OO mindset, this could be seen as encouraging one of the classic noob mistakes of the heyday of OO: believing that your code could be as complex as you wanted, without cost, as long as you hid the complicated bits inside member methods on your objects. I'm guessing that "write stupid code that uses smart objects" was a snappy bit of wisdom in the pre-OO days and was discarded as dangerous when the context of OO created a new and harmful way of interpreting it.
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
Always preferred Perlis' version, that might be slightly over-used in functional programming to justify all kinds of hijinks, but with some nuance works out really well in practice:
> 9. It is better to have 100 functions operate on one data structure than 10 functions on 10 data structures.
>I will, in fact, claim that the difference between a bad programmer
and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.
I think this is sometimes a barrier to getting started for me. I know that I need to explore the data structure design in the context of the code that will interact with it and some of that code will be thrown out as the data structure becomes more clear, but still it can be hard to get off the ground when me gut instinct is that the data design isn't right.
This kind of exploration can be a really positive use case for AI I think, like show me a sketch of this design vs that design and let's compare them together.
> This kind of exploration can be a really positive use case for AI I think
Not sure if SoTA codegen models are capable of navigating design space and coming up with optimal solutions. Like for cybersecurity, may be specialized models (like DeepMind's Sec-Gemini), if there are any, might?
I reckon, a programmer who already has learnt about / explored the design space, will be able to prompt more pointedly and evaluate the output qualitatively.
Yeah key word is exploration. It's not "hey Claude write the design doc for me" but rather, here's two possible directions for how to structure my solution, help me sketch each out a bit further so that I can get a better sense what roadblocks I may hit 50-100 hours into implementation when the cost of changing course is far greater.
As I'm sure more and more people are using AI to document old systems, even just to get a foothold in them personally if they don't intend to share it, here's a hint related to that: By default, if you fire an AI at a programming base, at least in my experience you get the usual documentation you expect from a system: This is the list of "key modules", this module does this, this module does that, this module does the other thing.
This is the worst sort of documentation; technically true but quite unenlightening. It is, in the parlance of the Fred Brooks quote mentioned in a sibling comment, neither the "flowchart" nor the "tables"; it is simply a brute enumeration of code.
To which the fix is, ask for the right thing. Ask for it to analyze the key data structures (tables) and provide you the flow through the program (the flowchart). It'll do it no problem. Might be inaccurate, as is a hazard with all documentation, but it makes as good a try at this style of documentation as "conventional" documentation.
Honestly one of the biggest problems I have with AI coding and documentation is just that the training set is filled to the brim with mediocrity and the defaults are inferior like this on numerous fronts. Also relevant to this conversation is that AI tends to code the same way it documents and it won't have either clear flow charts or tables unless you carefully prompt for them. It's pretty good at doing it when you ask, but if you don't ask you're gonna get a mess.
(And I find, at least in my contexts, using opus, you can't seem to prompt it to "use good data structures" in advance, it just writes scripting code like it always does and like that part of the prompt wasn't there. You pretty much have to come back in after its first cut and tell it what data structures to create. Then it's really good at the rest. YMMV, as is the way of AI.)
"Show me your flowchart and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowchart; it'll be obvious." -- Fred Brooks, The Mythical Man Month (1975)
This is the biggest issue I see with AI driven development. The data structures are incredibly naive. Yes it's easy to steer them in a different direction but that comes at a long term cost. The further you move from naive the more often you will need to resteer downstream and no amount of context management will help you, it is fighting against the literal mean.
Naive doesn't mean bad. 99% of software can be written with understood, well documented data structures. One of the problems with ai is that it allows people to create software without understanding the trade offs of certain data structures, algorithms and more fundamental hardware management strategies.
You don't need to be able to pass a leet code interview, but you should know about big O complexity, you should be able to work out if a linked list is better than an array, you should be able to program a trie, and you should be at least aware of concepts like cache coherence / locality. You don't need to be an expert, but these are realities of the way software and hardware work. They're also not super complex to gain a working knowledge of, and various LLMs are probably a really good way to gain that knowledge.
Then don't let the AI write the data structures. I don't. I usually don't even let the AI write the class or method names. I give it a skeleton application and let it fill in the code. Works great, and I retain knowledge of how the application works.
> This is the biggest issue I see with AI driven development. The data structures are incredibly naive.
Bill Gates, for example, always advocated for thinking through the entire program design and data structures before writing any code, emphasizing that structure is crucial to success.
And Paul Allen wrote a whole Altair emulator so that they could use an (academic) Harvard computer for their little (commercial) project and test/run Bill Gates' BASIC interpreter on it.
Aren't they basically saying opposite things? Perlis is saying "don't choose the right data structure, shoehorn your data into the most popular one". This advice might have made sense before generic programming was widespread; I think it's obsolete.
I find languages like Haskell, ReScript/OCaml to work really well for CRUD applications because they push you to think about your data and types first. Then you think about the transformations you want to make on the data via functions. When looking at new code I usually look for the types first, specifically what is getting stored and read.
Similarly, that approach works really well in Clojure too, albeit with a lot less concern for types, but the "data and data structures first" principle is widespread in the ecosystem.
One part of it has new resonance in the era of agentic LLMs:
alankay on June 21, 2016 | root | parent | next [–]
This is why "the objects of the future" have to be ambassadors that can negotiate with other objects they've never seen.
Think about this as one of the consequences of massive scaling ...
Nowdays rather than the methods associated with data objects, we are dealing with "context" and "prompts".
Hm, not sure. Data on its own (say, a string of numbers) might be meaningless - but structured data? Sure, there may be ambiguity but well-structured data generally ought to have a clear/obvious interpretation. This is the whole idea of nailing your data structures.
Yeah, structured data implies some processing on raw data to improve its meaning. Alan Kay seems to want to push this idea to encapsulate data with rich behaviour.
This quote from “Dive into Python” when I was a fresh graduate was one of the most impacting lines I ever read in a programming book.
> Busywork code is not important. Data is important. And data is not difficult. It's only data. If you have too much, filter it. If it's not what you want, map it. Focus on the data; leave the busywork behind.
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
If I have learned one thing in my 30-40 years spent writing code, it is this.
With 100 functions and one datastructure it is almost as programming with a global variables where new instance is equivalent to a new process. Doesn’t seem like a good rule to follow.
The scope of where that data structure or functions are available is a different concern though, "100 functions + 1 data structure" doesn't require globals or private, it's a separate thing.
As much as relational DBs have held back enterprise software for a very long time by being so conservative in their development, the fact that they force you to put this relationship absolutely front-of-mind is excellent.
I'd personally consider "persistence" AKA "how to store shit" to be a very different concern compared to the data structures that you use in the program. Ideally, your design shouldn't care about how things are stores, unless there is a particular concern for how fast things read/writes.
Also basically everything DHH ever said (I stopped using Rails 15 years ago but just defining data relationships in YAML and typing a single command to get a functioning website and database was in fact pretty cool in the oughts).
Hang on, they mostly agree with each other. I've spoken to Rob Pike a few times and I never heard him call out Perlis as being wrong. On this particular point, Perlis and Pike are both extending an existing idea put forward by Fred Brooks.
Perlis is right in the way that academics so often are and Pike is right in the way that practitioners often are. They also happen to be in rough agreement on this, unsurprisingly so.
Promoting the idea of one data structure with many functions contradicts:
“If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident.”
And:
“Use simple algorithms as well as simple data structures.”
A data structure general enough to solve enough problems to be meaningful will either be poorly suited to some problems or have complex algorithms for those problems, or both.
There are reasons we don’t all use graph databases or triple stores, and rely on abstractions over our byte arrays.
Can't agree more on 5. I've repeatedly found that any really tricky programming problem is (eventually) solved by iterative refinement of the data structures (and the APIs they expose / are associated with). When you get it right the control flow of a program becomes straightforward to reason about.
To address our favorite topic: while I use LLMs to assist on coding tasks a lot, I think they're very weak at this. Claude is much more likely to suggest or expand complex control flow logic on small data types than it is to recognize and implement an opportunity to encapsulate ideas in composable chunks. And I don't buy the idea that this doesn't matter since most code will be produced and consumed by LLMs. The LLMs of today are much more effective on code bases that have already been thoughtfully designed. So are humans. Why would that change?
I feel like 1 and 2 are only applicable in cases of novelty.
The thing is, if you build enough of the same kinds of systems in the same kinds of domains, you can kinda tell where you should optimize ahead of time.
Most of us tend to build the same kinds of systems and usually spend a career or a good chunk of our careers in a given domain. I feel like you can't really be considered a staff/principal if you can't already tell ahead of time where the perf bottleneck will be just on experience and intuition.
I feel like every time I have expected an area to be the major bottleneck it has been. Sometimes some areas perform worse than I expected, usually something that hasn't been coded well, but generally its pretty easy to spot the computationally heavy or many remote call areas well before you program them.
I have several times done performance tests before starting a project to confirm it can be made fast enough to be viable, the entire approach can often shift depending on how quickly something can be done.
It really depends on your requirements. C10k requires different design than a web server that sees a few requests per second at most, but the web might never have been invented if the focus was always on that level of optimization.
The number 1 issue Ive experienced with poor programmers is a belief that theyre special snowflakes who can anticipate the future.
It's the same thing with programmers who believe in BDUF or disbelieve YAGNI - they design architectures for anticipated futures which do not materialize instead of evolving the architecture retrospectively in line with the future which did materialize.
I think it's a natural human foible. Gambling, for instance, probably wouldnt exist if humans' gut instincts about their ability to predict future defaulted to realistic.
This is why no matter how many brilliant programmers scream YAGNI, dont do BDUF and dont prematurely optimize there will always be some comment saying the equivalent of "akshually sometimes you should...", remembering that one time when they metaphorically rolled a double six and anticipated the necessary architecture correctly when it wasnt even necessary to do so.
These programmers are all hopped up on a different kind of roulette these days...
Aye. The number one way to make software amenable to future requirements is to keep it simple so that it's easy to change in future. Adding complexity for anticipated changes works against being able to support the unanticipated ones.
Rob Pike is responsible for many cool things, but Unix isn't one of them. Go is a wonderful hybrid (with its own faults) of the schools of Thompson and Wirth, with a huge amount of Pike.
If you'd said Plan 9 and UTF-8 I'd agree with you.
Rob Pike definitely wrote large chunks of Unix while at Bell Labs. It's wrong to say he wrote all of it like the GP did but it is also wrong to diminish his contributions.
Unix was created by Ken Thompson and Dennis Ritchie at Bell Labs (AT&T) in 1969. Thompson wrote the initial version, and Ritchie later contributed significantly, including developing the C programming language, which Unix was subsequently rewritten in.
> but was a contributor to it. He, with a team, unquestionably wrote it.
contribute < wrote.
His credits are huge, but I think saying he wrote Unix is misattribution.
Credits include: Plan 9 (successor to Unix), Unix Window System, UTF-8 (maybe his most universally impactful contribution), Unix Philosophy Articulation, strings/greps/other tools, regular expressions, C successor work that ultimately let him to Go.
Are you under the impression he was, like, a hands-off project manager or something? His involvement was in writing it. Not singlehandedly, but certainly as part of a team. He unquestionably wrote it. He did not envision it like he did the other projects you mention.
The first four are kind of related. For me the fifth is the important – and oft overlooked – one:
> Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
These rules aged well overall. The only change I would make these days is to invert the order.
Number 5 is timeless and relevant at all scales, especially as code iterations have gotten faster and faster, data is all the more relevant. Numbers 4 and 3 have shifted a bit since data sizes and performance have ballooned, algorithm overhead isn't quite as big a concern, but the simplicity argument is relevant as ever. Numbers 2 and 1 while still true (Amdahl's law is a mathematical truth after all), are also clearly a product of their time and the hard constraints programmers had to deal with at the time as well as the shallowness of the stack. Still good wisdom, though I think on the whole the majority of programmers are less concerned about performance than they should be, especially compared to 50 years ago.
The attribution to Hoare is a common error — "Premature optimization is the root of all evil" first appeared in Knuth's 1974 paper "Structured Programming with go to Statements."
Knuth later attributed it to Hoare, but Hoare said he had no recollection of it and suggested it might have been Dijkstra.
Rule 5 aged the best. "Data dominates" is the lesson every senior engineer eventually learns the hard way.
I'm not a skilled programmer (but would like to be someday). Would someone kindly resolve what appears to me to be a contradiction between the following?
1(a) Torvalds: "Bad programmers worry about the code. Good programmers worry about data structures and their relationships."
1(b) Pike Rule 5: "Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming."
— versus —
2. Perlis 2: "Functions delay binding; data structures induce binding. Moral: Structure data late in the programming process."
---
Ignorant as I am, I read these to advise that I ought to put data structures centrally, first, foremost — but not until the end of the programming process.
How good is your model at picking good data structures?
There’s several orders of magnitude less available discussion of selecting data structures for problem domains than there is code.
If the underlying information is implicit in high volume of code available then maybe the models are good at it, especially when driven by devs who can/will prompt in that direction. And that assumption seems likely related to how much code was written by devs who focus on data.
> There’s several orders of magnitude less available discussion of selecting data structures for problem domains than there is code.
I believe that’s what most algorithms books are about. And most OS book talks more about data than algorithms. And if you watch livestream or read books on practical projects, you’ll see that a lot of refactor is first selecting a data structure, then adapt the code around it. DDD is about data structure.
- ideologically, he's spent his career chasing complexity reduction, adovcating for code sobriety, resource efficiency, and clarity of thought. Large, opaque, energy-intensive LLMs represent the antithesis.
Potentially its by either (or even both independently). Knuth originally attributed it to Hoare, but there's no paper trail to demonstrate Hoare actually coined it first
Rule 4, I have always practiced and demanded of junior programmers, to make algorithms and structures that are simple to understand, for our main user: the one who will modify this code in the future.
I believe that's why Golang is a very simple but powerful language.
> We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.
I think for people starting out - rule 5 isn't perhaps that obvious.
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
If want to solve a problem - it's natural to think about logic flow and the code that implements that first and the data structures are an after thought, whereas Rule 5 is spot on.
Conputers are machines that transform an input to an output.
> If want to solve a problem - it's natural to think about logic flow and the code that implements that first and the data structures are an after thought, whereas Rule 5 is spot on.
It is?
How can you conceive of a precise idea of how to solve a problem without a similarly precise idea of how you intend to represent the information fundamental to it? They are inseparable.
Obviously they are linked - the question is where do you start your thinking.
Do you start with the logical task first and structure the data second, or do you actually think about the data structures first?
Let's say I have a optimisation problem - I have a simple scoring function - and I just want to find the solution with the best score. Starting with the logic.
for all solutions, score, keep if max.
Simple eh? Problem is it's a combinatorial solution space. The key to solving this before the entropic death of the universe is to think about the structure of the solution space.
I mean - no. If you're coming to a completely new domain you have to decide what the important entities are, and what transformations you want to apply.
Neither data structures nor algorithms, but entities and tasks, from the user POV, one level up from any kind of implementation detail.
There's no point trying to do something if you have no idea what you're doing, or why.
When you know the what and why you can start worrying about the how.
Iff this is your 50th CRUD app you can probably skip this stage. But if it's green field development - no.
Sure context is important - and the important context you appear to have missed is the 5 rules aren't about building websites. It's about solving the kind of problems which are easy to state but hard to do (well) .
Also, "why these 5 in particular" is definitely not obvious -- there are a great many possible "obvious in some sense but also true in an important way" epigrams to choose from (the Perlis link from another comment has over a hundred). That Pike picked these 5 to emphasise tells you something about his view of programming, and doubly so given that they are rather overlapping in what they're talking about.
Yes, and I'd say it's more true now than then. Best case, your fancy algorithms are super-sizing code that runs 1% of the time, always kicking more-often-run code out of the most critical CPU caches. Worst case, your fancy algorithms contain security bugs, and the bad guys cash in.
9front it's distilled Unix. I corrected Russ Cox' 'xword' to work in 9front and I am just a newbie. No LLM's, that's Idiocratic, like the movie; just '9intro.us.pdf' and man pages.
"Premature optimization is the root of all evil."
First, let's not besmirch the good name of Tony Hoare. The quote is from Donald Knuth, and the missing context is essential.
From his 1974 paper, "Structured Programming with go to Statements":
"Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%."
He was talking about using GOTO statements in C. He was talking about making software much harder to reason about in the name of micro-optimizations. He assumed (incorrectly) that we would respect the machines our software runs on.
Multiple generations of programmers have now been raised to believe that brutally inefficient, bloated, and slow software is just fine. There is no limit to the amount of boilerplate and indirection a computer can be forced to execute. There is no ceiling to the crystalline abstractions emerging from these geniuses. There is no amount of time too long for a JVM to spend starting.
I worked at Google many years ago. I have lived the absolute nightmares that evolve from the willful misunderstanding of this quote.
No thank you. Never again.
I have committed these sins more than any other, and I'm mad as hell about it.
Far too often we generalise a piece of logic that we need in one or two places, making things more complicated for ourselves whenever they inevitably start to differ. And chances are very slim we will actually need it more than twice.
Premature generalisation is the most common mistake that separates a junior developer from an experienced one.
Instead, I tend to ask: if I change this code here, will I always also need to change it over there?
Copy-paste is good as long as I'm just repeating patterns. A for loop is a pattern. I use for loops in many places. That doesn't mean I need to somehow abstract out for loops because I'm repeating myself.
But if I have logic that says that button_b.x = button_a.x + button_a.w + padding, then I should make sure that I only write that information down once, so that it stays consistent throughout the program.
Oh yes, I'd recommend everyone who uses the phrase reads the rest of the paper to see the kinds of optimisations that Knuth considers justified. For example, optimising memory accesses in quicksort.
Tips like "don't try to write smart code" are often repeated but useless (not to mention that "smart" here means over-engineered or overly complex, not smart).
I wish Knuth would come out and publicly chastise the many decades of abuse this quote has enabled.
Profiling never achieved its place in most developers’ core loop the way that compiling, linting, or unit testing did.
How many real CI/CD pipelines spit out flame graphs alongside test results?
Maybe I’ve had an unrepresentative career, but I’ve never worked anywhere where there’s much time to fiddle with performance optimisations, let alone those that make the code/system significantly harder to understand. I expect that’s true of most people working in mainstream tech companies of the last twenty years or so. And so that quote is basically never applicable.
(AI will probably make this worse as well, having a bloat tendency all of its own)
I think that's due to people doing premature optimization! If people took the quote to heart, they would be less inclined to increasing the amount of boilerplate and indirection.
While you were seeing those problems with Java at Google, I saw seeing it with Python.
So many levels of indirection. Holy cow! So many unneeded superclasses and mixins! You can’t reason about code if the indirection is deeper than the human mind can grasp.
There was also a belief that list comprehensions were magically better somehow and would expand to 10-line monstrosities of unreadable code when a nested for loop would have been more readable and just as fast but because list comprehensions were fetishized nobody would stop at their natural readability limits. The result was like reading the run-on sentence you just suffered through.
Like you, I've seen people produce a lot of slow code, but it's mostly been from people who would have a really hard time writing faster code that's less wrong.
I hate slow software, but I'd pick it anytime over bogus software. Also, generally, it's easier to fix performance problems than incorrect behavior, especially so when the error has created data that's stored somewhere we might not have access to. But even more so, when the harm has reached the real world.
We can and should have both.
This is a fraud, made up by midwits to justify their leaning towers of abstraction.
Same. I, too, am sick of bloated code. But I use the quote as a reminder to myself: "look, the fact that you could spend the rest of the workday making this function run in linear instead of quadratic time doesn't mean you should – you have so many other tasks to tackle that it's better that you leave the suboptimal-but-obviously-correct implementation of this one little piece as-is for now, and return to it later if you need to".
For example, I've often heard "premature optimization is the root of all evil" invoked to support opposite sides of the same argument. Pike's rules are much clearer and harder to interpret creatively.
Also, it's amusing that you don't hear this anymore:
> Rule 5 is often shortened to "write stupid code that uses smart objects".
In context, this clearly means that if you invest enough mental work in designing your data structures, it's easy to write simple code to solve your problem. But interpreted through an OO mindset, this could be seen as encouraging one of the classic noob mistakes of the heyday of OO: believing that your code could be as complex as you wanted, without cost, as long as you hid the complicated bits inside member methods on your objects. I'm guessing that "write stupid code that uses smart objects" was a snappy bit of wisdom in the pre-OO days and was discarded as dangerous when the context of OO created a new and harmful way of interpreting it.
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
Always preferred Perlis' version, that might be slightly over-used in functional programming to justify all kinds of hijinks, but with some nuance works out really well in practice:
> 9. It is better to have 100 functions operate on one data structure than 10 functions on 10 data structures.
>I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.
-- Linus Torvalds
This kind of exploration can be a really positive use case for AI I think, like show me a sketch of this design vs that design and let's compare them together.
Not sure if SoTA codegen models are capable of navigating design space and coming up with optimal solutions. Like for cybersecurity, may be specialized models (like DeepMind's Sec-Gemini), if there are any, might?
I reckon, a programmer who already has learnt about / explored the design space, will be able to prompt more pointedly and evaluate the output qualitatively.
> sometimes a barrier to getting started for me
Plenty great books on the topic (:
Algorithms + Data Structures = Programs (1976), https://en.wikipedia.org/wiki/Algorithms_%2B_Data_Structures...
This is the worst sort of documentation; technically true but quite unenlightening. It is, in the parlance of the Fred Brooks quote mentioned in a sibling comment, neither the "flowchart" nor the "tables"; it is simply a brute enumeration of code.
To which the fix is, ask for the right thing. Ask for it to analyze the key data structures (tables) and provide you the flow through the program (the flowchart). It'll do it no problem. Might be inaccurate, as is a hazard with all documentation, but it makes as good a try at this style of documentation as "conventional" documentation.
Honestly one of the biggest problems I have with AI coding and documentation is just that the training set is filled to the brim with mediocrity and the defaults are inferior like this on numerous fronts. Also relevant to this conversation is that AI tends to code the same way it documents and it won't have either clear flow charts or tables unless you carefully prompt for them. It's pretty good at doing it when you ask, but if you don't ask you're gonna get a mess.
(And I find, at least in my contexts, using opus, you can't seem to prompt it to "use good data structures" in advance, it just writes scripting code like it always does and like that part of the prompt wasn't there. You pretty much have to come back in after its first cut and tell it what data structures to create. Then it's really good at the rest. YMMV, as is the way of AI.)
"Show me your flowchart and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won't usually need your flowchart; it'll be obvious." -- Fred Brooks, The Mythical Man Month (1975)
You don't need to be able to pass a leet code interview, but you should know about big O complexity, you should be able to work out if a linked list is better than an array, you should be able to program a trie, and you should be at least aware of concepts like cache coherence / locality. You don't need to be an expert, but these are realities of the way software and hardware work. They're also not super complex to gain a working knowledge of, and various LLMs are probably a really good way to gain that knowledge.
Bill Gates, for example, always advocated for thinking through the entire program design and data structures before writing any code, emphasizing that structure is crucial to success.
While developing Altair BASIC, his choice of data structures and algorithms enabled him to fit the code into just 4 kilobytes.
Microsoft is another story.
Perlin: stringly typed logic is great!
My interpretation of his point of view is that what you need is a process/interpreter/live object that 'explains' the data.
https://news.ycombinator.com/item?id=11945722
EDIT: He writes more about it in Quora. In brief, he says it is 'meaning', not 'data' that is central to programming.
https://qr.ae/pCVB9m
One part of it has new resonance in the era of agentic LLMs:
alankay on June 21, 2016 | root | parent | next [–]
This is why "the objects of the future" have to be ambassadors that can negotiate with other objects they've never seen. Think about this as one of the consequences of massive scaling ...
Nowdays rather than the methods associated with data objects, we are dealing with "context" and "prompts".
I should probably be thinking more in this direction.
> Busywork code is not important. Data is important. And data is not difficult. It's only data. If you have too much, filter it. If it's not what you want, map it. Focus on the data; leave the busywork behind.
> 2. Functions delay binding; data structures induce binding. Moral: Structure data late in the programming process.
If I have learned one thing in my 30-40 years spent writing code, it is this.
https://ocw.mit.edu/courses/6-001-structure-and-interpretati...
which I found very helpful in (finally) managing to get through that entire text (and do all the exercises).
That's great
Pike is right.
I would guess Pike is simply wise enough not to get involved in such arguments.
“If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident.”
And:
“Use simple algorithms as well as simple data structures.”
A data structure general enough to solve enough problems to be meaningful will either be poorly suited to some problems or have complex algorithms for those problems, or both.
There are reasons we don’t all use graph databases or triple stores, and rely on abstractions over our byte arrays.
To address our favorite topic: while I use LLMs to assist on coding tasks a lot, I think they're very weak at this. Claude is much more likely to suggest or expand complex control flow logic on small data types than it is to recognize and implement an opportunity to encapsulate ideas in composable chunks. And I don't buy the idea that this doesn't matter since most code will be produced and consumed by LLMs. The LLMs of today are much more effective on code bases that have already been thoughtfully designed. So are humans. Why would that change?
The thing is, if you build enough of the same kinds of systems in the same kinds of domains, you can kinda tell where you should optimize ahead of time.
Most of us tend to build the same kinds of systems and usually spend a career or a good chunk of our careers in a given domain. I feel like you can't really be considered a staff/principal if you can't already tell ahead of time where the perf bottleneck will be just on experience and intuition.
I have several times done performance tests before starting a project to confirm it can be made fast enough to be viable, the entire approach can often shift depending on how quickly something can be done.
It's the same thing with programmers who believe in BDUF or disbelieve YAGNI - they design architectures for anticipated futures which do not materialize instead of evolving the architecture retrospectively in line with the future which did materialize.
I think it's a natural human foible. Gambling, for instance, probably wouldnt exist if humans' gut instincts about their ability to predict future defaulted to realistic.
This is why no matter how many brilliant programmers scream YAGNI, dont do BDUF and dont prematurely optimize there will always be some comment saying the equivalent of "akshually sometimes you should...", remembering that one time when they metaphorically rolled a double six and anticipated the necessary architecture correctly when it wasnt even necessary to do so.
These programmers are all hopped up on a different kind of roulette these days...
Rules are "kinda" made to be broken. Be free.
I've been sticking to these rules (and will keep sticking to them) for as long as I can program (I've been doing it for the last 30 years).
IMHO, you can feel that a bottleneck is likely to occur, but you definitely can't tell where, when, or how it will actually happen.
If you'd said Plan 9 and UTF-8 I'd agree with you.
Unless you meant to imply that UNIX isn't cool.
Unix was created by Ken Thompson and Dennis Ritchie at Bell Labs (AT&T) in 1969. Thompson wrote the initial version, and Ritchie later contributed significantly, including developing the C programming language, which Unix was subsequently rewritten in.
contribute < wrote.
His credits are huge, but I think saying he wrote Unix is misattribution.
Credits include: Plan 9 (successor to Unix), Unix Window System, UTF-8 (maybe his most universally impactful contribution), Unix Philosophy Articulation, strings/greps/other tools, regular expressions, C successor work that ultimately let him to Go.
> Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
This is probably the worst use of the word "shortened" ever, and it should be more like "mutilated"?
Number 5 is timeless and relevant at all scales, especially as code iterations have gotten faster and faster, data is all the more relevant. Numbers 4 and 3 have shifted a bit since data sizes and performance have ballooned, algorithm overhead isn't quite as big a concern, but the simplicity argument is relevant as ever. Numbers 2 and 1 while still true (Amdahl's law is a mathematical truth after all), are also clearly a product of their time and the hard constraints programmers had to deal with at the time as well as the shallowness of the stack. Still good wisdom, though I think on the whole the majority of programmers are less concerned about performance than they should be, especially compared to 50 years ago.
Knuth later attributed it to Hoare, but Hoare said he had no recollection of it and suggested it might have been Dijkstra.
Rule 5 aged the best. "Data dominates" is the lesson every senior engineer eventually learns the hard way.
edit: s/data/data structure/
Good software can handle crap data.
1(a) Torvalds: "Bad programmers worry about the code. Good programmers worry about data structures and their relationships."
1(b) Pike Rule 5: "Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming."
— versus —
2. Perlis 2: "Functions delay binding; data structures induce binding. Moral: Structure data late in the programming process."
---
Ignorant as I am, I read these to advise that I ought to put data structures centrally, first, foremost — but not until the end of the programming process.
So not really a contradiction, just Perlis talking about the functional shell and Torvalds/Pike talking about the imperative core.
Good structure comes from exploring until you understand the problem well AND THEN letting data structure dominate.
There’s several orders of magnitude less available discussion of selecting data structures for problem domains than there is code.
If the underlying information is implicit in high volume of code available then maybe the models are good at it, especially when driven by devs who can/will prompt in that direction. And that assumption seems likely related to how much code was written by devs who focus on data.
I believe that’s what most algorithms books are about. And most OS book talks more about data than algorithms. And if you watch livestream or read books on practical projects, you’ll see that a lot of refactor is first selecting a data structure, then adapt the code around it. DDD is about data structure.
Based on everything public, Pike is deeply hostile to generative AI in general:
- The Christmas 2025 incident (https://simonwillison.net/2025/Dec/26/slop-acts-of-kindness/)
- he's labeled GenAI as nuclear waste (https://www.webpronews.com/rob-pike-labels-generative-ai-nuc...)
- ideologically, he's spent his career chasing complexity reduction, adovcating for code sobriety, resource efficiency, and clarity of thought. Large, opaque, energy-intensive LLMs represent the antithesis.
I believe that's why Golang is a very simple but powerful language.
This Axiom has caused far and away more damage to software development than the premature optimization ever will.
> We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.
https://news.ycombinator.com/item?id=47325225
Funny handwritten html artifact though:
> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
If want to solve a problem - it's natural to think about logic flow and the code that implements that first and the data structures are an after thought, whereas Rule 5 is spot on.
Conputers are machines that transform an input to an output.
It is?
How can you conceive of a precise idea of how to solve a problem without a similarly precise idea of how you intend to represent the information fundamental to it? They are inseparable.
Do you start with the logical task first and structure the data second, or do you actually think about the data structures first?
Let's say I have a optimisation problem - I have a simple scoring function - and I just want to find the solution with the best score. Starting with the logic.
for all solutions, score, keep if max.
Simple eh? Problem is it's a combinatorial solution space. The key to solving this before the entropic death of the universe is to think about the structure of the solution space.
Neither data structures nor algorithms, but entities and tasks, from the user POV, one level up from any kind of implementation detail.
There's no point trying to do something if you have no idea what you're doing, or why.
When you know the what and why you can start worrying about the how.
Iff this is your 50th CRUD app you can probably skip this stage. But if it's green field development - no.
eg sort a list.
That's why a collection of "obvious" things formulated in a convincing way by a person with big street cred is still useful and worth elevating.
"Why quote someone who's just quoting someone else?" — Michael Scott — knorker
LLM's work will never be reproducible by design.